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About This Role
Location: Manayunk, Charlotte, San Antonio, Jacksonville, Alpharetta.
At EY, we’re all in to shape your future with confidence.
We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.
Senior Analyst \- Financial Services Organization – AI and Data – Service Delivery Center
EY is the only professional services firm with a separate business unit (“FSO”) that is dedicated to the financial services marketplace. Our FSO teams have been at the forefront of every event that has reshaped and redefined the financial services industry. This practice also has several Service Delivery Centers that are made up of high\-performing US\-based resources who work closely with our experienced client\-serving professionals to deliver project\-based work and managed services to our US\-based Financial Services clients. If you have a passion for rallying together to solve the most complex challenges in the financial services industry, come join our dynamic FSO team!
The Opportunity
You’ll architect and deploy production\-grade AI agents and multi\-agent systems that transform financial services. Working with cutting\-edge LLMs and agentic frameworks, you’ll build autonomous systems that process millions of transactions, make intelligent decisions, and continuously learn from real\-world financial data. This is your chance to shape how AI reshapes global finance.
What You’ll Do
Build Autonomous Financial AI Systems
Design and implement multi\-agent architectures using LangChain, AutoGen, or CrewAI for complex financial workflows Deploy production\-ready LLMs fine\-tuned for financial domain expertise (loan underwriting, risk assessment, regulatory compliance) Create RAG (Retrieval\-Augmented Generation) systems that connect AI agents to enterprise knowledge bases and real\-time market data Implement agentic reasoning systems capable of autonomous decision\-making within regulatory boundaries
Scale AI Solutions for Enterprise Impact
- Deploy AI agents serving millions of customers with sub\-second latency requirements
- Build robust MLOps pipelines for continuous model improvement and A/B testing
- Implement comprehensive monitoring and observability for autonomous systems Optimize inference costs while maintaining performance SLAs
Drive Innovation in Financial AI
- Prototype breakthrough applications: AI\-powered trading assistants, autonomous compliance monitors, intelligent fraud detection agents
- Collaborate with financial domain experts to translate complex regulations into AI agent behaviors
- Contribute to EY’s AI research initiatives and patent applications
- Present solutions to C\-suite executives at major financial institutions
Technical Requirements
Core Skills (Must\-Have)
- 3\-5 years of Python programming with production deployment experience
- Hands\-on experience with LLMs: GPT\-4, Claude, Llama, or similar foundation models
- Agentic AI frameworks: LangChain, AutoGen, CrewAI, or similar multi\-agent orchestration tools
- Vector databases: Pinecone, Weaviate, or Chroma for semantic search at scale
- Cloud platforms: AWS SageMaker, Azure OpenAI Service, or Google Vertex AI
- MLOps practices: Model versioning, A/B testing, drift detection, and continuous deployment
Preferred Skills (Nice\-to\-Have)
- Experience with financial services applications (trading, risk, compliance, or banking)
- Knowledge of prompt engineering and in\-context learning optimization
- Familiarity with model fine\-tuning techniques (LoRA, QLoRA, PEFT)
- Understanding of AI safety practices and responsible AI frameworks
- Experience with real\-time streaming architectures (Kafka, Flink)
- Contributions to open\-source AI projects
- Good written and verbal communication skills
- A willingness and ability to travel at 0%\-25%
- Valid driver’s license in the US
- Valid passport
Ideally, you’ll also have
- Certification in any database management system, reporting or data visualization, or programming/statistical language
- Working knowledge or certifications in cloud technologies such as Snowflake, Databricks, AWS, or Azure
- Experience with generative AI models and techniques, including GANs and Transformers
- Understanding of the ethical implications of generative AI and commitment to responsible AI practices
What we look for
We’re interested in professionals that are passionate about technology, who already have an understanding of data and are comfortable analyzing or manipulating it while generating reports for clients. You’ll also need to be able to clearly articulate both problems and proposed solutions, and have the willingness to learn and quickly adapt to changing requirements. On top of this, we’re looking for team players and hard workers who are not afraid to take initiative to master their craft and produce high\-quality work. If you have a proactive approach and want to be part of a group that continues to grow significantly, this role is for you.
What working at EY offers
We offer a competitive compensation package where you’ll be rewarded based on your performance and recognized for the value you bring to our business. In addition, our Total Rewards package includes medical and dental coverage, both pension and 401(k) plans, a minimum of 15 days of vacation plus ten observed holidays and three paid personal days, and a range of programs and benefits designed to support your physical, financial, and social well\-being. Plus, we offer:
- Opportunities to develop new skills and progress your career
- Support and coaching from some of the most engaging and knowledgeable colleagues
- A collaborative environment where everyone works together to create a better working world
- Excellent training and development prospects
About EY
As a global leader in assurance, tax, transaction, and advisory services, we hire and develop the most passionate people in their field to help build a better working world. This starts with a culture that believes in giving you the training, opportunities, and creative freedom to make things better. So that whenever you join, however long you stay, the exceptional EY experience lasts a lifetime.
Join us in building a better working world. Apply now.
What we offer you
At EY, we’ll develop you with future\-focused skills and equip you with world\-class experiences. We’ll empower you in a flexible environment, and fuel you and your extraordinary talents in a diverse and inclusive culture of globally connected teams. Learn more.
- We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job in all geographic locations in the US is $65,500 to $134,000\. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $78,600 to $152,100\. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography. In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
- Join us in our team\-led and leader\-enabled hybrid model. Our expectation is for most people in external, client serving roles to work together in person 40\-60% of the time over the course of an engagement, project or year.
- Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances. You’ll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well\-being.
Are you ready to shape your future with confidence? Apply today.
EY accepts applications for this position on an on\-going basis.
For those living in California, please click here for additional information.
EY focuses on high\-ethical standards and integrity among its employees and expects all candidates to demonstrate these qualities.
EY \| Building a better working world
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.
Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.
EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi\-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.
EY provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, genetic information, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law.
EY is committed to providing reasonable accommodation to qualified individuals with disabilities including veterans with disabilities. If you have a disability and either need assistance applying online or need to request an accommodation during any part of the application process, please call 1\-800\-EY\-HELP3, select Option 2 for candidate related inquiries, then select Option 1 for candidate queries and finally select Option 2 for candidates with an inquiry which will route you to EY’s Talent Shared Services Team (TSS) or email the TSS at ssc.customersupport@ey.com.
Salary Context
This $65K-$152K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At EY, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($108K) sits 35% below the category median. Disclosed range: $65K to $152K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
EY AI Hiring
EY has 20 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Architect, AI Product Manager. Positions span Indianapolis, IN, US, Seattle, WA, US, Jacksonville, FL, US. Compensation range: $152K - $374K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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